Image processing device and method, recording medium and program

ABSTRACT

There is provided an image processing device including a motion vector detection portion that performs comparison of a substantially spherical photographic subject such that, among a plurality of captured images including the photographic subject, an image as a processing target and another image as a comparison target are compared using each of the plurality of captured images as the processing target, and which detects a motion vector of a whole three-dimensional spherical model with respect to the processing target, a motion compensation portion that performs motion compensation on the processing target, based on the motion vector of each of the plurality of captured images that is detected by the motion vector detection portion, and a synthesis portion that synthesizes each of the captured images that are obtained as a result of the motion compensation performed by the motion compensation portion.

BACKGROUND

The present technology relates to an image processing device and method,a recording medium and a program, and particularly relates to an imageprocessing device and method, a recording medium and a program that arecapable of easily improving image quality of an image obtained bycapturing a substantially spherical body, using a simple configuration.

A fundus examination is known in which a fundus oculi, such as a retinain an eyeball, an optic papilla or the like, is observed through apupil. The fundus examination is performed using a special device, suchas a funduscope or a fundus camera, for example. The fundus examinationis performed such that, for example, when an image of the fundus oculiin the eyeball of a test subject is captured by a fundus camera and aresultant captured image (hereinafter referred to as a fundus oculiimage) is displayed on a monitor or the like, an observer observes thefundus oculi image. In order for the observer to perform accurateobservation, the image quality of the fundus oculi image is improved.

As a known technique to improve the image quality of the fundus oculiimage, there is a technique in which, for example, data of a pluralityof fundus oculi images that are sequentially captured are synthesizedwhile taking into account the fact that the eyeball is a substantialsphere. With this technique, the plurality of fundus oculi images thatare captured over a certain period of time are synthesized. Therefore,if the fundus oculi moves in the certain period of time, the imagequality improvement of the fundus oculi image is hindered. To addressthis, Japanese Patent Application Publication No. JP-A-2011-087672discloses a technique that performs alignment of rotation directions ofa plurality of three-dimensional images of a fundus oculi. Thethree-dimensional images are formed using tomographic images of thefundus oculi that are obtained by optical coherence tomography (OCT).Further, for example, Japanese Patent Application Publication No.JP-A-2010-269016 discloses a technique that uses an affinetransformation to perform alignment including rotation of a plurality offundus oculi images.

SUMMARY

However, with the technique described in Japanese Patent ApplicationPublication No. JP-A-2011-087672, the tomographic images by OCT arerequired in order to form the three-dimensional images, resulting in anincrease in the device size. Further, with the technique described inJapanese Patent Application Publication No. JP-A-2010-269016, the affinetransformation, which is used for rotation of two-dimensional images, isused for the fundus oculi images. As a result, it is difficult toaccurately perform alignment of the images of the eyeball that is athree-dimensional sphere.

In summary, in recent years, there is a demand to easily obtain a fundusoculi image with a high image quality using a simple configuration.However, this demand is not sufficiently satisfied by known technologiesincluding those described in Japanese Patent Application Publication No.JP-A-2011-087672 and Japanese Patent Application Publication No.JP-A-2010-269016. The above circumstances apply not only to the fundusoculi image, but also apply to an image obtained by capturing asubstantially spherical body.

The present technology has been devised in light of the abovecircumstances, and makes it possible to easily improve image quality ofan image obtained by capturing a substantially spherical body, using asimple configuration.

According to an embodiment of the present technology, there is providedan image processing device including a motion vector detection portionthat performs comparison of a substantially spherical photographicsubject such that, among a plurality of captured images including thephotographic subject, an image as a processing target and another imageas a comparison target are compared using each of the plurality ofcaptured images as the processing target, and which detects a motionvector of a whole three-dimensional spherical model with respect to theprocessing target, a motion compensation portion that performs motioncompensation on the processing target, based on the motion vector ofeach of the plurality of captured images that is detected by the motionvector detection portion, and a synthesis portion that synthesizes eachof the captured images that are obtained as a result of the motioncompensation performed by the motion compensation portion.

With respect to each of a plurality of blocks that are divided up fromthe processing target, the motion vector detection portion may detect alocal motion vector by performing block matching with the comparisontarget. The motion vector detection portion may detect the motion vectorof the whole three-dimensional spherical model with respect to theprocessing target, using the local motion vector of each of theplurality of blocks.

The motion vector detection portion may convert the local motion vectorwith respect to each of the plurality of blocks in the processing targetinto a local spherical motion vector in the three-dimensional sphericalmodel. The motion vector detection portion may detect the motion vectorof the whole three-dimensional spherical model with respect to theprocessing target, using the local spherical motion vector of each ofthe plurality of blocks.

The motion vector detection portion may convert each of the plurality ofblocks in the processing target into a plurality of spherical blocks inthe three-dimensional spherical model. With respect to each of theplurality of spherical blocks, the motion vector detection portion maydetect, as the local motion vector, a local spherical motion vector byperforming block matching with the comparison target. The motion vectordetection portion may detect the motion vector of the wholethree-dimensional spherical model with respect to the processing target,using the local spherical motion vector of each of the plurality ofspherical blocks.

The motion vector detection portion may convert each of the processingtarget and the comparison target into a spherical image in thethree-dimensional spherical model. The motion vector detection portionmay perform matching between the spherical image of the processingtarget and the spherical image of the comparison target, and thereby maydetect the motion vector of the whole three-dimensional spherical modelwith respect to the processing target.

The photographic subject may be a fundus oculi.

The three-dimensional spherical model may be switched and used inaccordance with conditions of the photographic subject.

An image processing method, a recording medium and a program accordingto the embodiment of the present technology are the image processingmethod, the recording medium and the program corresponding to the imageprocessing device according to the embodiment of the present technologydescribed above.

In the image processing device and method, the recording medium and theprogram according to the embodiment of the present technology,comparison of a substantially spherical photographic subject isperformed such that, among a plurality of captured images including thephotographic subject, an image as a processing target and another imageas a comparison target are compared using each of the plurality ofcaptured images as the processing target, a motion vector of a wholethree-dimensional spherical model with respect to the processing targetis detected, motion compensation on the processing target is performed,based on the motion vector of each of the plurality of captured imagesthat is detected, and each of the captured images that are obtained as aresult of the motion compensation is synthesized.

According to another embodiment of the present technology, there isprovided an image processing device including a conversion portion that,among a plurality of captured images that include a substantiallyspherical photographic subject, converts an image as a processing targetand another image as a comparison target into spherical images on athree-dimensional spherical model, using each of the plurality ofcaptured images as the processing target, an extraction portion thatextracts features of each of the spherical image of the processingtarget and the spherical image of the comparison target, an alignmentportion that aligns positions of the features such that the featuresmatch each other, and a synthesis portion that synthesizes each of thecaptured images that are obtained as a result of the alignment performedby the alignment portion.

A blood vessel shape may be used as the feature.

The photographic subject may be a fundus oculi.

The three-dimensional spherical model may be switched and used inaccordance with conditions of the photographic subject.

An image processing method, a recording medium and a program accordingto another embodiment of the present technology are the image processingmethod, the recording medium and the program corresponding to the imageprocessing device according to the embodiment of the present technologydescribed above.

In the image processing device and method, the recording medium and theprogram according to the another embodiment of the present technology,among a plurality of captured images that include a substantiallyspherical photographic subject, an image as a processing target andanother image as a comparison target are converted into spherical imageson a three-dimensional spherical model, using each of the plurality ofcaptured images as the processing target, features of each of thespherical image of the processing target and the spherical image of thecomparison target are extracted, positions of the features are alignedsuch that the features match each other, and each of the captured imagesthat are obtained as a result of the alignment are synthesized.

According to the present technology described above, it is possible toeasily improve image quality of an image obtained by capturing asubstantially spherical body, using a simple configuration.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a configuration example of a fundusoculi image processing device to which the present technology isapplied;

FIG. 2 is a block diagram showing a configuration example of a motionvector detection portion;

FIG. 3 is a diagram illustrating specific processing of the motionvector detection portion;

FIG. 4 is a flowchart illustrating a flow of fundus oculi imagegeneration processing;

FIG. 5 is a flowchart illustrating a flow of motion vector detectionprocessing;

FIG. 6 is a block diagram showing a configuration example of a motionvector detection portion;

FIG. 7 is a diagram illustrating specific processing of the motionvector detection portion;

FIG. 8 is a flowchart illustrating a flow of motion vector detectionprocessing;

FIG. 9 is a block diagram showing a configuration example of a motionvector detection portion;

FIG. 10 is a diagram illustrating specific processing of the motionvector detection portion;

FIG. 11 is a flowchart illustrating a flow of motion vector detectionprocessing;

FIG. 12 is a block diagram showing a configuration example of a fundusoculi image processing device;

FIG. 13 is a diagram illustrating specific processing of a featureextraction portion and an alignment portion;

FIG. 14 is a diagram showing a configuration example of a blood vesselalignment processing portion;

FIG. 15 is a diagram illustrating specific processing of the bloodvessel alignment processing portion;

FIG. 16 is a flowchart illustrating a flow of fundus oculi imagegeneration processing;

FIG. 17 is a flowchart illustrating a flow of blood vessel alignmentprocessing; and

FIG. 18 is a block diagram showing a hardware configuration example ofan image processing device to which the present technology is applied.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

Hereinafter, four embodiments (hereinafter, respectively referred to asfirst to fourth embodiments) of the present technology will be explainedin the following order.

1. First embodiment (an example in which a motion vector detected fromeach block is applied to a spherical model)

2. Second embodiment (an example in which a motion vector of each blockapplied to the spherical model is detected)

3. Third embodiment (an example in which a motion vector is detectedfrom a fundus oculi image applied to the spherical model)

4. Fourth embodiment (an example in which fundus oculi images applied tothe spherical model are aligned)

First Embodiment

Configuration Example of Fundus Oculi Image Processing Device

FIG. 1 is a block diagram showing a configuration example of a fundusoculi image processing device to which the present technology isapplied.

A fundus oculi image processing device 10 shown in FIG. 1 captures animage of a fundus oculi, such as a retina in an eyeball, an opticpapilla or the like, of a test subject. The fundus oculi imageprocessing device 10 performs image processing on data of the obtainedfundus oculi image to improve image quality, and causes the fundus oculiimage after the image processing to be displayed.

In order to reduce a burden on the test subject, the fundus oculi imageprocessing device 10 captures an image of a photographic subject(namely, the fundus oculi of the test subject) while suppressing anamount of light irradiated onto the photographic subject. As a result,data of one sheet of a fundus oculi image can be obtained. However, inorder to obtain a higher quality fundus oculi image, the fundus oculiimage processing device 10 performs image capture of the photographicsubject a plurality of times, and performs various types of imageprocessing, which will be described later, on data of a plurality ofcaptured images obtained each time. As a result of performing suchvarious types of image processing, a fundus oculi image with higherimage quality is displayed by the fundus oculi image processing device10.

Note that the single image capture described herein means a series ofoperations of the fundus oculi image processing device 10 until light isaccumulated on each of pixels of an imaging element and an electricalsignal (data of each pixel) is output from each of the pixels. In thiscase, a number of times of image capture and a time interval betweeneach image capture are not particularly limited. For example, if thetime interval between each image capture is reduced to be approximately1/30 seconds and image capture is performed 300 times consecutively atthat time interval, 10 seconds of moving images are obtained. Insummary, a plurality of times of image capture described herein is aconcept including image capture to obtain a plurality of still imagesand image capture to obtain moving images.

The fundus oculi image processing device 10 configured in this mannerincludes an imaging portion 21, an image processing portion 22, astorage portion 23 and an output portion 24.

The imaging portion 21 captures an image of the fundus oculi of the testsubject located at a predetermined position, as a photographic subject,and outputs data of the obtained fundus oculi image. For example, theimaging portion 21 can have a configuration including a charge coupleddevice (CCD) imaging element, a complementary metal oxide semiconductor(CMOS) imaging element and the like. However, the imaging portion 21 isnot limited to this configuration, and it can have any configuration aslong as it can output data of the fundus oculi image. Note that, in thepresent embodiment, the imaging portion 21 has a function of irradiatinglight onto the photographic subject that is being captured, in order toobtain a fundus oculi image with higher image quality.

As a technique to obtain a fundus oculi image with higher image quality,a technique is known in which the amount of light irradiated onto thefundus oculi of the test subject is increased during image capture.However, if the amount of light irradiated onto the fundus oculi of thetest subject is increased during image capture, a burden on the testsubject is increased. As a result, there is a possibility that anunnecessary influence will be exerted on an observation target or apsychological burden on the test subject will be increased. Further, inthis case, since an increased amount of light is irradiated onto thefundus oculi of the test subject during image capture, the test subjectfeels that it is too bright and closes his/her eye or moves and there isa possibility that the fundus oculi image with high image quality cannotbe obtained. To address this, in the present embodiment, the amount oflight irradiated onto the fundus oculi is not increased during imagecapture, and the imaging portion 21 repeats image capture of the fundusoculi a plurality of times while maintaining a state in whichlow-intensity light is irradiated onto the fundus oculi. Note that imagecapture of the fundus oculi by the imaging portion 21 may be performed aplurality of times to obtain still images or may be performed once toobtain moving images.

Each of the fundus oculi images obtained by the single image capture inthis manner has low image quality because the light irradiated duringimage capture is weak (dark). For this reason, in the presentembodiment, the image processing portion 22 performs predetermined imageprocessing on data of the fundus oculi images obtained by each of theplurality of times of image capture by the imaging portion 21, and thusgenerates and outputs data of one sheet of a fundus oculi image withhigh image quality. Note that, in this case, the improvement in imagequality becomes easier when the plurality of fundus oculi images, whichare image processing targets, are more similar to each other. Therefore,it is preferable to reduce a time taken to perform a plurality of timesof imaging operations of the imaging portion 21.

The image processing portion 22 causes the data of the fundus oculiimage after the image processing, namely, the data of the fundus oculiimage with high image quality, to be stored in the storage portion 23 orto be output from the output portion 24.

The storage portion 23 is formed by, for example, a hard disk, a flashmemory or a random access memory (RAM), and stores the data of thefundus oculi image supplied from the image processing portion 22. Thedata of the fundus oculi image stored in the storage portion 23 is readby a playback portion or the like (not shown in the drawings), and isoutput to the output portion 24 for display or transmitted to anotherdevice. Other image processing is performed on the data of the fundusoculi image by an image processing portion (not shown in the drawings)other than the image processing portion 22.

The output portion 24 includes a monitor, such as a cathode ray tube(CRT), a liquid crystal display (LCD) or the like, and an outputterminal etc. The output portion 24 outputs and displays on the monitorthe data of the fundus oculi image output from the image processingportion 22, or outputs the data to an external device from the outputterminal of the output portion 24.

Further, hereinafter, a detailed configuration of the image processingportion 22 included in the fundus oculi image processing device 10 shownin FIG. 1 will be explained. The image processing portion 22 includes aninput image buffer 31, a motion vector detection portion 32, a motioncompensation portion 33, a synthesis portion 34 and a super-resolutionprocessing portion 35.

The input image buffer 31 is configured as one region of a given storagemedium, such as a hard disk, a flash memory or a RAM, for example. Theinput image buffer 31 holds data of fundus oculi images with low imagequality that are sequentially supplied from the imaging portion 21, asrespective data of input images. The data of each of the input images isread out from the input image buffer 31 at a predetermined timing and issupplied to the motion vector detection portion 32 or the motioncompensation portion 33.

The data of the fundus oculi images obtained by the imaging portion 21are obtained by performing image capture a plurality of times, and thefundus oculi images are not necessarily exactly the same as each other.For example, it is conceivable that a positional displacement occurs insome or all of the fundus oculi images. Accordingly, if the plurality offundus oculi images are simply synthesized, there is a possibility thatthe resultant fundus oculi image becomes a blurred image or a doubleimage due to the positional displacement or the like. Therefore, beforethe synthesis portion 34 synthesizes the data of the plurality of fundusoculi images, it is necessary that the motion vector detection portion32 detects a motion vector and the motion compensation portion 33performs motion compensation using the motion vector to thereby reduce adifference (a positional displacement etc.) between the images to besynthesized.

The motion vector detection portion 32 reads out data of a processingtarget input image and data of an input image that is captured at adifferent time from the processing target input image, from the inputimage buffer 31. Next, the motion vector detection portion 32 comparesthe read-out data of the two sheets of input images and thereby detectsa motion vector of the whole fundus oculi in the processing target inputimage. Note that, as a motion vector detection technique, the presentembodiment adopts a technique in which the eyeball is modeled as athree-dimensional sphere and a motion vector of the whole sphere isdetected. More specifically, among the plurality of captured imagesincluding the substantially spherical photographic subject, the motionvector detection portion 32 performs comparison of the captured image asa processing target and the captured image as a comparison target, usinga three-dimensional spherical model (hereinafter simply referred to as aspherical model) of the photographic subject. The comparison isperformed using each of the plurality of captured images as a processingtarget. This technique will be described later with reference to FIG. 2,along with a detailed configuration of the motion vector detectionportion 32.

The motion compensation portion 33 reads out the data of the processingtarget input image from the input image buffer 31. At the same time, themotion compensation portion 33 acquires, from the motion vectordetection portion 32, the motion vector of the whole fundus oculi in theprocessing target input image. Then, the motion compensation portion 33uses the motion vector of the whole fundus oculi to perform motioncompensation on the data of the processing target input image. Themotion compensation is processing that moves the processing target inputimage on the spherical model in accordance with the motion vector of thewhole fundus oculi in the processing target input image. By doing this,a difference (a positional displacement etc.) between the plurality offundus oculi images is reduced. Data of fundus oculi images after themotion compensation is supplied to the synthesis portion 34.

In this manner, the respective data of the plurality of fundus oculiimages obtained by the imaging portion 21 performing image capture aplurality of times are sequentially used as a processing target, andafter the motion compensation portion 33 performs motion compensation onthe respective data, they are sequentially supplied to the synthesisportion 34.

When all the data of the plurality of fundus oculi images are suppliedfrom the motion compensation portion 33, the synthesis portion 34synthesizes the data of the plurality of fundus oculi images and therebygenerates data of one sheet of a fundus oculi image. The synthesisportion 34 supplies the generated data to the super-resolutionprocessing portion 35.

The super-resolution processing portion 35 performs super-resolutionprocessing on the data of the fundus oculi image synthesized by thesynthesis portion 34, and thereby generates data of the fundus oculiimage with a higher resolution than that at the time of synthesis. Notethat any processing method can be used to perform the super-resolutionprocessing by the super-resolution processing portion 35. For example, amethod described in Japanese Patent Application Publication No.JP-A-2010-102696, or a method described in Japanese Patent ApplicationPublication No. JP-A-2010-103981 may be used to perform thesuper-resolution processing. Note however that, in the super-resolutionprocessing, processing in accordance with features of a living organismis performed so that a higher-resolution image with less noise can beobtained. The data of the high-resolution fundus oculi image generatedin this manner by the super-resolution processing portion 35 is storedin the storage portion 23 or output from the output portion 24.

Next, a detailed configuration of the motion vector detection portion 32will be explained.

Configuration Example and Processing of Motion Vector Detection Portion

FIG. 2 is a block diagram showing a configuration example of the motionvector detection portion 32. FIG. 3 is a diagram illustrating specificprocessing of the motion vector detection portion 32.

As shown in FIG. 2, the motion vector detection portion 32 includes alocal motion vector detection portion 41, a spherical motion vectorconversion portion 42, a spherical model storage portion 43 and a fundusoculi motion vector detection portion 44.

The local motion vector detection portion 41 reads out data of aprocessing target fundus oculi image 51-i and data of a comparisontarget fundus oculi image 51-j that is different from the processingtarget, from among respective data of n sheets of fundus oculi images51-l to 51-n that are respectively held as input image data in the inputimage buffer 31 as shown in FIG. 3. Here, n is an integer equal to orlarger than 2 and indicates a total number of the input images held inthe input image buffer 31. i is an integer equal to or larger than 1 andequal to or smaller than n−1. j is an integer equal to or larger than 1and equal to or smaller than n, and is an integer different from theinteger i. For example, in the present embodiment, j=i+1 is set. Insummary, an image adjacent to a processing target image is used as acomparison target.

The local motion vector detection portion 41 divides up the processingtarget fundus oculi image 51-i into a plurality of blocks having aprescribed size, and sequentially sets each of the plurality of blocksas a processing target block 61-i.

The local motion vector detection portion 41 also divides up thecomparison target fundus oculi image 51-j into a plurality of blockshaving a prescribed size, in a similar manner to the above. Each timethe local motion vector detection portion 41 sets each of the pluralityof blocks as a comparison target block 61-j in a raster scan order, forexample, the local motion vector detection portion 41 repeatedlycalculates a degree of similarity between the processing target block61-i and the comparison target block 61-j. In summary, so-called blockmatching is performed.

The local motion vector detection portion 41 detects local motion in theprocessing target block 61-i, as a motion vector my, based on apositional relationship between the processing target block 61-i and thecomparison target block 61-j matched with (namely, having a highestdegree of similarity with) the processing target block 61-i.

The spherical motion vector conversion portion 42 applies the processingtarget block 61-i to a spherical model 63 that is stored in thespherical model storage portion 43, as shown in FIG. 3. By doing this,the processing target block 61-i is converted into a predetermined block64-i on the spherical model 63. Note that the converted block 64-i ishereinafter referred to as a spherical processing target block 64-i. Inthis case, the motion vector my in the processing target block 61-i isconverted into a motion vector mvr in the spherical processing targetblock 64-i. Note that the motion vector mvr after the conversion ishereinafter referred to as a spherical motion vector mvr.

This type of the spherical motion vector mvr is obtained by repeatedlyperforming the above-described series of processing for each of theplurality of blocks divided up from the processing target fundus oculiimage 51-i.

With respect to the processing target fundus oculi image 51-i, thefundus oculi motion vector detection portion 44 detects a motion vector65 of the whole fundus oculi, based on the spherical motion vector mvrof each of the plurality of blocks. The fundus oculi motion vectordetection portion 44 supplies to the motion compensation portion 33 themotion vector 65 of the whole fundus oculi in the processing targetfundus oculi image 51-i.

Thus, motion compensation using the motion vector 65 of the whole fundusoculi is performed on the data of the processing target fundus oculiimage 51-i by the motion compensation portion 33, as described abovewith reference to FIG. 1.

The configuration of the fundus oculi image processing device 10 isexplained above with reference to FIG. 1 to FIG. 3. Next, processing(hereinafter referred to as fundus oculi image generation processing)that is performed by the fundus oculi image processing device 10configured as described above will be explained with reference to FIG.4.

Flow of Fundus Oculi Image Generation Processing

FIG. 4 is a flowchart illustrating a flow of the fundus oculi imagegeneration processing.

At step S11, the imaging portion 21 reduces the amount of light andcaptures an image of the fundus oculi of the test subject a plurality oftimes. Note that, as described above, image capture of the fundus oculiby the imaging portion 21 may be performed a plurality of times toobtain still images or may be performed once to obtain moving images.

At step S12, the image processing portion 22 causes the input imagebuffer 31 to store the data of the plurality of fundus oculi images withlow image quality obtained by the processing at step S11.

At step S13, the motion vector detection portion 32 performs motionvector detection processing and detects the motion vector of the wholefundus oculi. Note that the motion vector detection processing will bedescribed in more detail later with reference to FIG. 5.

At step 14, the motion compensation portion 33 uses the motion vector ofthe whole fundus oculi detected by the processing at step S13 to performmotion compensation on the data of the processing target input imageread out from the input image buffer 31. Note that the motioncompensation portion 33 performs similar motion compensation also on thedata of the input image on which the processing has been performed.

At step S15, the image processing portion 22 determines whether or notall the data of the fundus oculi images have been set as the processingtarget. In the present embodiment, the data of the processing targetfundus oculi images are respective data of the fundus oculi images 51-lto 51-(n−1). Therefore, the data of the comparison target fundus oculiimages are respective data of the fundus oculi images 51-2 to 51-n.

When all the data of the fundus oculi images have not yet been set asthe processing target, NO is determined at step S15 and the processingis returned to step S13. Then, the processing from step S13 onward isrepeated. More specifically, loop processing from step S13 to step S15is repeated until all the data of the fundus oculi images are set as theprocessing target. For example, as described later with reference toFIG. 5, if a motion vector is detected for the fundus oculi image 51-2,which is the comparison target of the fundus oculi image 51-1 set as theprocessing target by the motion vector detection processing at step S13,then the fundus oculi image 51-2 is set as the processing target image,and a motion vector for an adjacent comparison target fundus oculi image51-3 is detected. This type of processing is sequentially repeated.

After that, when all the data of the fundus oculi images have been setas the processing target, YES is determined at step S15 and theprocessing proceeds to step S16.

At step S16, the synthesis portion 34 synthesizes the motion compensateddata of the plurality of the fundus oculi images. Thus, data of onesheet of a fundus oculi image is generated from the data of the n sheetsof fundus oculi images.

At step S17, the super-resolution processing portion 35 performssuper-resolution processing on the synthesized data of the fundus oculiimage. Thus, data of the fundus oculi image with a higher resolutionthan that at the time of synthesis at step S16 is generated.

At step 518, the image processing portion 22 causes the storage portion23 to store the data of the fundus oculi image on which thesuper-resolution processing has been performed, or causes the outputportion 24 to output the data.

This completes the fundus oculi image generation processing. Next, themotion vector detection processing at step S 13 will be explained.

Flow of Motion Vector Detection Processing

FIG. 5 is a flowchart illustrating a flow of the motion vector detectionprocessing performed at step S13 shown in FIG. 4.

At step S31, the local motion vector detection portion 41 reads out dataof two sheets of fundus oculi images, which are a processing target anda comparison target, from the input image buffer 31. For example, dataof two sheets of adjacent fundus oculi images, the fundus oculi image51-1 and the fundus oculi image 51-2, are read out.

At step S32, the local motion vector detection portion 41 sets aprocessing target block from a processing target image (for example, thefundus oculi image 51-1) and performs block matching with a comparisontarget fundus oculi image (for example, the fundus oculi image 51-2). Asa result, a motion vector in the processing target block is detected.

At step S33, the spherical motion vector conversion portion 42 appliesthe motion vector detected at step S32 to the spherical model stored inthe spherical model storage portion 43, and thereby converts the motionvector to a spherical motion vector.

At step S34, the motion vector detection portion 32 determines whetheror not all the blocks have been processed. More specifically, the motionvector detection portion 32 determines whether or not spherical motionvectors corresponding to all the blocks divided up from a sheet of theprocessing target fundus oculi image (for example, the sheet of thefundus oculi image 51-1 read out by the processing at step S31) havebeen detected.

When all the blocks have not yet been processed, NO is determined atstep S34 and the processing is returned to step S32. Then, theprocessing from step S32 onward is repeated. More specifically, loopprocessing from step S32 to step S34 is repeated until all the blocksare processed.

After that, when all the blocks have been processed, YES is determinedat step S34 and the processing proceeds to step S35.

At step S35, with respect to the processing target fundus oculi image(for example, the sheet of the fundus oculi image 51-1 read out by theprocessing at step S31), the fundus oculi motion vector detectionportion 44 detects a motion vector of the whole fundus oculi based onthe respective spherical motion vectors of the plurality of blocks.

This completes the motion vector detection processing.

In this manner, the image processing using a spherical model isperformed on the plurality of fundus oculi images that are obtainedwhile suppressing the amount of irradiated light in order to reduce aburden on the test subject. Thus, data of one sheet of a fundus oculiimage with high image quality is generated.

In the present embodiment, general block matching is used as degree ofsimilarity calculation between the processing target block divided upfrom the processing target fundus oculi image and the comparison targetblock divided up from the comparison target fundus oculi image. Sincethis type of general block matching is applied, high-speed processingcan be easily achieved. Further, since the block matching is performedfor each of the processing target blocks, it is possible to reduce amemory amount to be used.

Second Embodiment

In the motion vector detection portion 32 of the first embodiment, aspherical model is applied to the motion vector in the processing targetblock detected by block matching. However, the target to which thespherical model is applied is not limited to this example. For example,the spherical model may be applied to the processing target block beforethe block matching is performed and the block matching may be performedthereafter.

Note that a fundus oculi image processing device according to a secondembodiment has basically the same function and configuration as those ofthe fundus oculi image processing device 10 shown in FIG. 1. Therefore,hereinafter, an explanation of same portions as those of the fundusoculi image processing device 10 shown in FIG. 1 is omitted, and adifferent portion only, namely, a motion vector detection portion 70that differs from the motion vector detection portion 32 of the fundusoculi image processing device 10 shown in FIG. 1, will be explained.

Configuration Example and Processing of Motion Vector Detection Portion70

FIG. 6 is a block diagram showing a configuration example of the motionvector detection portion 70. FIG. 7 is a diagram illustrating specificprocessing of the motion vector detection portion 70.

As shown in FIG. 6, the motion vector detection portion 70 includes alocal spherical motion vector detection portion 71, a spherical modelstorage portion 72 and a fundus oculi motion vector detection portion73.

The local spherical motion vector detection portion 71 reads out data ofa processing target fundus oculi image 81-i and data of a comparisontarget fundus oculi image 81-j that is different from the processingtarget, from among respective data of n sheets of fundus oculi images81-l to 81-n that are respectively held as input image data in the inputimage buffer 31, as shown in FIG. 7. Here, n is an integer equal to orlarger than 2 and indicates a total number of the input images held inthe input image buffer 31. i is an integer equal to or larger than 1 andequal to or smaller than n. j is an integer equal to or larger than 1and equal to or smaller than n, and is an integer different from theinteger i.

The local spherical motion vector detection portion 71 divides up theprocessing target fundus oculi image 81-i into a plurality of blockshaving a prescribed size, and sequentially sets each of the plurality ofblocks as a processing target block 91-i.

The local spherical motion vector detection portion 71 applies theprocessing target block 91-i to a spherical model 92 that is stored inthe spherical model storage portion 72, as shown in FIG. 7. By doingthis, the processing target block 91-i is converted into a predeterminedblock 93-i on the spherical model 92. Note that the converted block 93-iis hereinafter referred to as the processing target spherical block93-i.

The local spherical motion vector detection portion 71 also divides upthe comparison target fundus oculi image 81-j into a plurality of blockshaving a prescribed size, in a similar manner to the above. Each timethe local spherical motion vector detection portion 71 sets each of theplurality of blocks as a comparison target block 91-j in a raster order,for example, the local spherical motion vector detection portion 71 alsoapplies the comparison target block 91-j to the spherical model 92. Bydoing this, the comparison target block 91-j is converted into apredetermined block 93-j on the spherical model 92. Note that theconverted block 93-j is hereinafter referred to as the comparison targetspherical block 93-j.

The local spherical motion vector detection portion 71 repeatedlycalculates a degree of similarity between the processing targetspherical block 93-i and the comparison target spherical block 93-j. Insummary, so-called block matching is performed.

The local spherical motion vector detection portion 71 detects localmotion of the sphere in the processing target spherical block 93-i, asthe spherical motion vector mvr, based on a positional relationshipbetween the processing target spherical block 93-i and the comparisontarget spherical block 93-j matched (namely, having a highest degree ofsimilarity) with the processing target spherical block 93-i.

This type of the spherical motion vector mvr is obtained by repeatedlyperforming the above-described series of processing for each of theplurality of blocks divided up from the processing target fundus oculiimage 81-i.

With respect to the processing target fundus oculi image 81-i, thefundus oculi motion vector detection portion 73 detects a motion vector95 of the whole fundus oculi, based on the respective spherical motionvectors mvr of the plurality of blocks. The fundus oculi motion vectordetection portion 73 supplies the motion vector 95 of the whole fundusoculi in the processing target fundus oculi image 81-i to the motioncompensation portion 33.

Thus, motion compensation using the motion vector 95 of the whole fundusoculi is performed on the data of the processing target fundus oculiimage 81-i by the motion compensation portion 33, as described abovewith reference to FIG. 1.

Next, an explanation will be given of fundus oculi image generationprocessing of the fundus oculi image processing device 10 according tothe second embodiment that has the motion vector detection portion 70configured in this manner. The fundus oculi image generation processingaccording to the second embodiment is performed in accordance with theflowchart shown in FIG. 4, in a similar manner to the first embodiment.However, content of motion vector detection processing at step S13 inthe second embodiment is different from that in the first embodiment.Therefore, hereinafter, the motion vector detection processing at stepS13 in the second embodiment will be explained with reference to FIG. 8.

Flow of Motion Vector Detection Processing

FIG. 8 is a flowchart illustrating a flow of the motion vector detectionprocessing.

At step S51, the local spherical motion vector detection portion 71divides up the respective data of the two sheets of fundus oculi images(which are the processing target and the comparison target) read outfrom the input image buffer 31 into a plurality of blocks. The localspherical motion vector detection portion 71 applies each of theplurality of blocks to a spherical model and converts each of theplurality of blocks to a spherical block.

At step S52, the local spherical motion vector detection portion 71 setsa processing target spherical block from the processing target fundusoculi image.

At step S53, the local spherical motion vector detection portion 71performs block matching between the processing target spherical blockand a comparison target spherical block. Thus, a spherical motion vectorin the processing target spherical block is detected.

At step S54, the motion vector detection portion 70 determines whetheror not all the blocks have been processed. More specifically, the motionvector detection portion 70 determines whether or not spherical motionvectors corresponding to all the blocks divided up from one sheet of theprocessing target fundus oculi image have been detected.

When all the blocks have not yet been processed, NO is determined atstep S54 and the processing is returned to step S52. Then, theprocessing from step S52 onward is repeated. More specifically, loopprocessing from step S52 to step S54 is repeated until all the blocksare processed.

After that, when all the blocks have been processed, YES is determinedat step S54 and the processing proceeds to step S55.

At step S55, with respect to the processing target fundus oculi image,the fundus oculi motion vector detection portion 73 detects a motionvector of the whole fundus oculi based on the respective sphericalmotion vectors of the plurality of blocks.

This completes the motion vector detection processing.

In the present embodiment, block matching is performed after theprocessing target block divided up from the processing target fundusoculi image and the comparison target block divided up from thecomparison target fundus oculi image have each been applied to thespherical model. Therefore, the blocks on the spherical model are usedin the degree of similarity calculation between the processing targetfundus oculi image and the comparison target fundus oculi image. Thus,the motion vector of the whole fundus oculi, which is a substantiallyspherical body, can be detected accurately. Since the image processingusing this motion vector is performed on the captured fundus oculiimages, a fundus oculi image with higher image quality is generated incomparison with the first embodiment.

Third Embodiment

In the motion vector detection portions 32 and 70 according to the firstand second embodiments, the spherical model is applied to the processingtarget block divided up from the processing target image. However, thetarget to which the spherical model is applied is not limited to thisexample. For example, the spherical model may be applied to the wholefundus oculi image and thereafter matching may be performed.

Note that a fundus oculi image processing device according to a thirdembodiment has basically the same function and configuration as those ofthe fundus oculi image processing device 10 shown in FIG. 1. Therefore,hereinafter, an explanation of same portions as those of the fundusoculi image processing device 10 shown in FIG. 1 is omitted, and only adifferent portion, namely, a motion vector detection portion 100 thatdiffers from the motion vector detection portion 32 of the fundus oculiimage processing device 10 shown in FIG. 1, will be explained.

Configuration Example and Processing of Motion Vector Detection Portion100

FIG. 9 is a block diagram showing a configuration example of the motionvector detection portion 100. FIG. 10 is a diagram illustrating specificprocessing of the motion vector detection portion 100.

As shown in FIG. 9, the motion vector detection portion 100 includes afundus oculi sphere conversion portion 101, a spherical model storageportion 102 and a fundus oculi motion vector detection portion 103.

The fundus oculi sphere conversion portion 101 reads out data of aprocessing target fundus oculi image 111-i and data of a comparisontarget fundus oculi image 111-j that is different from the processingtarget, from among respective data of n sheets of fundus oculi images111-l to 111-n that are respectively held as input image data in theinput image buffer 31, as shown in FIG. 10. Here, n is an integer equalto or larger than 2 and indicates a total number of the input imagesheld in the input image buffer 31. i is an integer equal to or largerthan 1 and equal to or smaller than n−1. j is an integer equal to orlarger than 1 and equal to or smaller than n, and is an integerdifferent from the integer i. For example, in the present embodiment,j=i+1 is set.

As shown in FIG. 10, the fundus oculi sphere conversion portion 101applies the whole of the processing target fundus oculi image 111-i to aspherical model that is stored in the spherical model storage portion102. Thus, the processing target fundus oculi image 111-i is convertedinto a fundus oculi image 112 i on the spherical model. Note that theconverted fundus oculi image 112 i is hereinafter referred to as theprocessing target spherical fundus oculi image 112 i.

The fundus oculi sphere conversion portion 101 also applies a comparisontarget fundus oculi image 111-j to the spherical model stored in thespherical model storage portion 102, in a similar manner to the above.By doing this, the comparison target fundus oculi image 111-j isconverted into a fundus oculi image 113 j on the spherical model. Notethat the converted fundus oculi image 113 j is hereinafter referred toas the comparison target spherical fundus oculi image 113 j.

The fundus oculi motion vector detection portion 103 repeatedlycalculates a degree of similarity while rotating the processing targetspherical fundus oculi image 112 i and the comparison target sphericalfundus oculi image 113 j. In summary, matching is performed on the wholesphere. Here, a technique for matching between the processing targetspherical fundus oculi image 112 i and the comparison target sphericalfundus oculi image 113 j is not particularly limited.

The fundus oculi motion vector detection portion 103 detects a motionvector 114 of the whole fundus oculi, based on a positional relationshipbetween the processing target spherical fundus oculi image 112 i and thecomparison target spherical fundus oculi image 113 j matched (namely,having a highest degree of similarity) with the processing targetspherical fundus oculi image 112 i. The fundus oculi motion vectordetection portion 103 supplies, to the motion compensation portion 33,the motion vector 114 of the whole fundus oculi in the processing targetspherical fundus oculi image 112 i.

Thus, motion compensation using the motion vector 114 of the wholefundus oculi is performed on the data of the processing target fundusoculi image 111-i by the motion compensation portion 33, as describedabove with reference to FIG. 1.

Next, an explanation will be given of fundus oculi image generationprocessing of the fundus oculi image processing device 10 according tothe third embodiment that has the motion vector detection portion 100configured in this manner. The fundus oculi image generation processingaccording to the third embodiment is performed in accordance with theflowchart shown in FIG. 4, in a similar manner to the first embodiment.However, content of motion vector detection processing at step S13 inthe third embodiment is different from that in the first embodiment.Therefore, hereinafter, the motion vector detection processing at stepS13 in the third embodiment will be explained with reference to FIG. 11.

Flow of Motion Vector Detection Processing

FIG. 11 is a flowchart illustrating a flow of the motion vectordetection processing.

At step S71, the fundus oculi sphere conversion portion 101 reads outdata of two sheets of fundus oculi images, which are the processingtarget and the comparison target, from the input image buffer 31.

At step S72, the fundus oculi sphere conversion portion 101 applies theprocessing target whole fundus oculi image and the comparison targetwhole fundus oculi image read out at step S71 to the spherical modelstored in the spherical model storage portion 102. The fundus oculisphere conversion portion 101 converts them to spherical fundus oculiimages, respectively.

At step S73, the fundus oculi motion vector detection portion 103performs matching between the converted processing target sphericalfundus oculi image and the converted comparison target spherical fundusoculi image, and detects a motion vector of the whole fundus oculi.

This completes the motion vector detection processing.

In the present embodiment, matching is performed after the processingtarget whole fundus oculi image and the comparison target whole fundusoculi image have each been applied to the spherical model. Therefore,the whole fundus oculi images on the spherical model are used in thedegree of similarity calculation between the processing target fundusoculi image and the comparison target fundus oculi image. Thus, themotion vector of the whole fundus oculi, which is a substantiallyspherical body, can be detected accurately. Since the image processingusing this motion vector is performed on the captured fundus oculiimages, a fundus oculi image with higher image quality is generated incomparison with the second embodiment.

Fourth Embodiment

In the fundus oculi image processing devices 10 according to the firstto third embodiments, the motion vector of the whole fundus oculi isdetected by performing matching between the processing target fundusoculi image and the comparison target fundus oculi image. However, inmany cases, the fundus oculi image basically has a substantially uniformcolor in the whole image. Further, since the amount of irradiated lightis reduced, the fundus oculi image tends to be a relatively dark image.In addition, a plurality of times of image capture by the imagingportion 21 is performed in a relatively short time and under conditionsthat are as close to each other as possible. Therefore, an amount ofmotion between the plurality of fundus oculi images tends to berelatively small. Furthermore, even when there is a motion, it is rarethat some regions of the fundus oculi image show a significantly largemovement compared to the other regions, and substantially the wholeimage tends to move almost uniformly. Accordingly, detection of themotion vector may become difficult. To address this, instead ofdetecting the motion vector, alignment of the fundus oculi image may beperformed using biological information of the photographic subject, withrespect to the whole fundus oculi image.

In the present embodiment, for example, information of a blood vesselshape is used as the biological information of the photographic subjectthat is used for alignment of the fundus oculi image. Note that thebiological information of the photographic subject is not limited tothis example, and it may be any information. For example, information ofthe shape of a nerve, a nerve papilla or the like may be adopted as thebiological information of the photographic subject. Further, when anorgan or a cell is used as the photographic subject, information of theshape etc. of the cell or a nucleus of the cell may be adopted as thebiological information of the photographic subject. Furthermore, aplurality of types of biological information (for example, a bloodvessel and an optic papilla etc.) may be combined and adopted.

Configuration Example and Processing of Fundus Oculi Image ProcessingDevice

FIG. 12 is a block diagram showing a configuration example of a fundusoculi image processing device 200.

The fundus oculi image processing device 200 shown in FIG. 12 hasbasically the same function and configuration as those of the fundusoculi image processing device 10 shown in FIG. 1. Therefore,hereinafter, an explanation of same portions as those of the fundusoculi image processing device 10 shown in FIG. 1 is omitted, and only adifferent portion, namely, an image processing portion 212 that differsfrom the image processing portion 22 of the fundus oculi imageprocessing device 10 shown in FIG. 1, will be explained.

The image processing portion 212 includes an input image buffer 221, afundus oculi sphere conversion portion 222, a spherical model storageportion 223, a feature extraction portion 224, an alignment portion 225,a synthesis portion 226 and a super-resolution processing portion 227.

The input image buffer 221 has basically the same function andconfiguration as those of the input image buffer 31 shown in FIG. 1. Theinput image buffer 221 holds data of fundus oculi images with low imagequality that are sequentially supplied from the imaging portion 21, asrespective data of the input images. The data of each of the inputimages is read out from the input image buffer 221 at a predeterminedtiming and is supplied to the fundus oculi sphere conversion portion222.

The fundus oculi sphere conversion portion 222 has basically the samefunction and configuration as those of the fundus oculi sphereconversion portion 101 shown in FIG. 9. The fundus oculi sphereconversion portion 222 reads out data of a processing target fundusoculi image and data of a comparison target fundus oculi image that isdifferent from the processing target, from among respective data of aplurality of fundus oculi images that are respectively held as inputimage data in the input image buffer 221. Then, the fundus oculi sphereconversion portion 222 applies the processing target fundus oculi imageand the comparison target fundus oculi image to a spherical model thatis stored in the spherical model storage portion 223. Thus, theprocessing target fundus oculi image and the comparison target fundusoculi image are converted into fundus oculi images on the sphericalmodel. Note that the converted processing target fundus oculi image ishereinafter referred to as a processing target spherical fundus oculiimage. Further, the converted comparison target fundus oculi image ishereinafter referred to as a comparison target spherical fundus oculiimage. The fundus oculi sphere conversion portion 222 supplies theprocessing target spherical fundus oculi image and the comparison targetspherical fundus oculi image to the feature extraction portion 224.

The feature extraction portion 224 includes a blood vessel extractionportion 231 and an intersection extraction portion 233. The alignmentportion 225 includes a blood vessel alignment processing portion 232 andan intersection alignment processing portion 234. Specific processing ofthe feature extraction portion 224 and the alignment portion 225 will beexplained with reference to FIG. 13.

As shown in FIG. 13, the blood vessel extraction portion 231 extractsfeatures (processing 261, 262), such as the shape and position etc., ofa blood vessel, from each of a processing target spherical fundus oculiimage 251 and a comparison target spherical fundus oculi image 252supplied from the fundus oculi sphere conversion portion 222. At thistime, the blood vessel extraction portion 231 uses an R component of RGBcomponents to extract a blood vessel from each of the processing targetspherical fundus oculi image 251 and the comparison target sphericalfundus oculi image 252, as in a method described in “Fundus oculi imagesynthesis method using blood vessel features”, Katsuyoshi Tanabe,Tetsuro Tsubouchi, Hidenori Okuda, Masahiro Oku, 2007. The blood vesselextraction portion 231 supplies the features, such as the shape andposition etc., of the blood vessel extracted from each of the processingtarget spherical fundus oculi image 251 and the comparison targetspherical fundus oculi image 252 to the blood vessel alignmentprocessing portion 232, as a blood vessel extraction result of each ofthe images.

As shown in FIG. 13, the blood vessel alignment processing portion 232performs blood vessel alignment processing (processing 266) between theprocessing target spherical fundus oculi image 251 and the comparisontarget spherical fundus oculi image 252, using the blood vesselextraction result of the processing target spherical fundus oculi image251 and the blood vessel extraction result of the comparison targetspherical fundus oculi image 252 that are supplied from the blood vesselextraction portion 231.

The blood vessel alignment processing portion 232 supplies the synthesisportion 226 with a processing target spherical fundus oculi image 253 onwhich the blood vessel alignment processing has been performed. Notethat the configuration and processing of the blood vessel alignmentprocessing portion 232 will be explained in more detail later withreference to FIG. 14 and FIG. 15.

Further, before the blood vessel alignment processing (the processing266) is performed using the blood vessel extraction results, simplealignment may be performed using positions of blood vesselintersections. Note that the blood vessel intersection is a portion atwhich blood vessels intersect (including a case of a torsion position inactuality) in the fundus oculi image, or a portion at which the bloodvessels diverge.

In this case, as shown in FIG. 13, the blood vessel extraction portion231 supplies a blood vessel extraction result of the processing targetspherical fundus oculi image 251 that is obtained by the processing 261,to the intersection extraction portion 233. Further, as shown in FIG.13, the blood vessel extraction portion 231 supplies a blood vesselextraction result of the comparison target spherical fundus oculi image252 that is obtained by the processing 262, to the intersectionextraction portion 233.

As shown in FIG. 13, the intersection extraction portion 233 extracts anintersection (processing 263) using the blood vessel extraction resultof the processing target spherical fundus oculi image 251 that issupplied from the blood vessel extraction portion 231. Further, theintersection extraction portion 233 extracts an intersection (processing264) using the blood vessel extraction result of the comparison targetspherical fundus oculi image 252 that is supplied from the blood vesselextraction portion 231. The intersection extraction portion 233 suppliespositions of the intersections respectively extracted from theprocessing target spherical fundus oculi image 251 and the comparisontarget spherical fundus oculi image 252 to the intersection alignmentprocessing portion 234, as intersection extraction results.

As shown in FIG. 13, the intersection alignment processing portion 234performs intersection alignment processing (processing 265) between theprocessing target spherical fundus oculi image 251 and the comparisontarget spherical fundus oculi image 252, using the respectiveintersection extraction results of the processing target sphericalfundus oculi image 251 and the comparison target spherical fundus oculiimage 252 that are supplied from the intersection extraction portion233. The intersection alignment processing portion 234 supplies a resultof the intersection alignment processing to the blood vessel alignmentprocessing portion 232, as an intersection alignment result.

The blood vessel alignment processing portion 232 uses the intersectionalignment result supplied from the intersection alignment processingportion 234 as a spherical fundus oculi image in an initial state, andfurther performs blood vessel alignment processing (processing 266)using the blood vessel extraction results on the spherical fundus oculiimage in the initial state. More specifically, while performingalignment in a similar manner to the intersection alignment inaccordance with the intersection alignment result, the blood vesselalignment processing portion 232 superimposes the respective bloodvessel extraction results and sets the superimposed image as the initialstate.

In this manner, the blood vessel alignment processing portion 232 canfurther perform alignment using the blood vessel extraction results onthe spherical fundus oculi image for which alignment has been simplyperformed using the intersection. Therefore, the blood vessel alignmentprocessing portion 232 can perform alignment more easily and at a higherspeed.

Note that, further, alignment using other biological information may beadopted at the same time. For example, firstly, while performingalignment at a position of the optic papilla, alignment using theintersection may be further performed on the spherical fundus oculiimage in the initial state, using the spherical fundus oculi imageobtained by superimposing the processing target spherical fundus oculiimage 251 and the comparison target spherical fundus oculi image 252 asthe spherical fundus oculi image in the initial state.

When all the data of the plurality of spherical fundus oculi images thathave been aligned are supplied from the blood vessel alignmentprocessing portion 232, the synthesis portion 226 synthesizes therespective data of the plurality of spherical fundus oculi images andthereby generates data of one sheet of a fundus oculi image. Thesynthesis portion 226 supplies the generated data to thesuper-resolution processing portion 227.

The super-resolution processing portion 227 performs super-resolutionprocessing on the data of the fundus oculi image synthesized by thesynthesis portion 226, and thereby generates data of the fundus oculiimage with an even higher resolution than that at the time of synthesis.The data of the high-resolution fundus oculi image generated in thismanner by the super-resolution processing portion 227 is stored in thestorage portion 23 or output from the output portion 24.

Next, the configuration and processing of the blood vessel alignmentprocessing portion 232 will be explained in detail.

Configuration Example and Processing of Blood Vessel AlignmentProcessing Portion

FIG. 14 is a diagram showing a configuration example of the blood vesselalignment processing portion 232. FIG. 15 is a diagram illustratingspecific processing of the blood vessel alignment processing portion232.

As shown in FIG. 14, the blood vessel alignment processing portion 232includes a superimposition processing portion 271, a shift processingportion 272, a stretch processing portion 273, a rotation processingportion 274, a zoom-in/zoom-out processing portion 275, a convergencedetermination portion 276 and an adjustment portion 277.

The superimposition processing portion 271 superimposes the respectiveblood vessel extraction results of the processing target sphericalfundus oculi image 251 and the comparison target spherical fundus oculiimage 252 that are supplied from the blood vessel extraction portion231. When alignment using the intersection is performed, thesuperimposition processing portion 271 superimposes the respective bloodvessel extraction results while performing alignment in a similar mannerto the intersection alignment using the intersection alignment resultsupplied from the intersection alignment processing portion 234. Thesuperimposition processing portion 271 supplies a superimposed result tothe shift processing portion 272. Note that the blood vessel alignmentprocessing portion 232 performs alignment so that a blood vesselextraction result 292 approaches a blood vessel extraction result 291.

As shown in FIG. 15, the shift processing portion 272 performs avertical/horizontal shift 281 that causes the whole of the blood vesselextraction result 292 to move (shift) in a given direction, such as avertical direction or a horizontal direction. The shift processingportion 272 supplies a superimposed result to the stretch processingportion 273 in a state in which the blood vessel extraction result 292approaches the blood vessel extraction result 291 to a maximum extent.Although any method can be used to determine how close the blood vesselextraction result 291 and the blood vessel extraction result 292 are toeach other, a difference between absolute values of the two images, forexample, can be used for determination. More specifically, the shiftprocessing portion 272 causes the whole of the blood vessel extractionresult 292 to move (shift) and searches for a position at which thedifference between the absolute values of the blood vessel extractionresult 291 and the blood vessel extraction result 292 is minimum. Thisdetermination method also applies to the following processing portions.

As shown in FIG. 15, the stretch processing portion 273 performs avertical/horizontal stretch 282 that stretches (deforms) the bloodvessel extraction result 291 in a given direction, such as the verticaldirection or the horizontal direction. The stretch processing portion273 supplies a superimposed result to the rotation processing portion274 in a state in which the blood vessel extraction result 292approaches the blood vessel extraction result 291 to a maximum extent.For example, the stretch processing portion 273 stretches (deforms) theblood vessel extraction result 292 in a given direction, and searchesfor a shape that causes the difference between the absolute values ofthe blood vessel extraction result 291 and the blood vessel extractionresult 292 to be minimum.

As shown in FIG. 15, the rotation processing portion 274 performs arotation 283 that rotates the blood vessel extraction result 292 in leftand right directions, and supplies a superimposed result to thezoom-in/zoom-out processing portion 275 in a state in which the bloodvessel extraction result 292 approaches the blood vessel extractionresult 291 to a maximum extent. For example, the rotation processingportion 274 rotates the blood vessel extraction result 292 in the leftand right directions, and searches for a direction in which thedifference between the absolute values of the blood vessel extractionresult 291 and the blood vessel extraction result 292 is minimum.

As shown in FIG. 15, the zoom-in/zoom-out processing portion 275performs a zoom-in/zoom-out 284 that zooms in or zooms out the bloodvessel extraction result 292, and supplies a superimposed result to theconvergence determination portion 276 in a state in which the bloodvessel extraction result 292 approaches the blood vessel extractionresult 291 to a maximum extent. For example, the zoom-in/zoom-outprocessing portion 275 zooms in or zooms out the blood vessel extractionresult 292, and searches for a size that causes the difference betweenthe absolute values of the blood vessel extraction result 291 and theblood vessel extraction result 292 to be minimum.

The convergence determination portion 276 determines whether or not thealignment has converged, based on the supplied superimposed result. Forexample, the convergence determination portion 276 causes each of theabove-described processing to be repeatedly performed, and compares analignment result obtained this time with an alignment result of theprevious time. When the blood vessel extraction result 292 is closer tothe blood vessel extraction result 291 than the previous time, theconvergence determination portion 276 determines that the alignment hasnot converged. When the blood vessel extraction result 292 is not closerto the blood vessel extraction result 291 than the previous time (forexample, when the difference between the absolute values of the bloodvessel extraction result 291 and the blood vessel extraction result 292is not smaller than the previous time), the convergence determinationportion 276 determines that the alignment has converged.

When it is determined that the alignment has not converged (for example,when the difference between the absolute values of the blood vesselextraction result 291 and the blood vessel extraction result 292 issmaller than the previous time), the convergence determination portion276 returns the superimposed result to the shift processing portion 272and causes the alignment to be performed again.

When it is determined that the alignment has converged, the adjustmentportion 277 performs adjustment of the alignment based on cumulativeconvergence results obtained until the previous time. For example, it isassumed that first to fifth fundus oculi images, which are in a timeascending order, are obtained by performing image capture five timesconsecutively. In this case, when the fifth fundus oculi image is set asthe processing target, the fourth fundus oculi image is used as thecomparison target. Note that the convergence result is a result ofperforming alignment such that the fifth fundus oculi image approachesthe fourth fundus oculi image. In summary, at a stage when theconvergence is complete, the fifth fundus oculi image has onlyapproached the fourth fundus oculi image. However, in the synthesisportion 266 that will be described later, an image (an aligned image)obtained by causing the fifth fundus oculi image to approach the firstfundus oculi image is used as a synthesis target. Therefore, it isnecessary to cause the image immediately after the convergence, namely,the image (the aligned image) obtained by causing the fifth fundus oculiimage to approach the fourth fundus oculi image, to further approach thefirst fundus oculi image (namely, it is necessary to adjust thealignment).

In order to cause the fourth fundus oculi image to approach the firstfundus oculi image (namely, in order to perform adjustment of thealignment), it is necessary to use the cumulative convergence resultsobtained until the previous time. Specifically, adjustment of thealignment to cause the fourth fundus oculi image to approach the firstfundus oculi image is performed by cumulatively using a convergenceresult (a last convergence result) obtained by causing the fifth fundusoculi image to approach the fourth fundus oculi image, a convergenceresult (a second last convergence result) obtained by causing the fourthfundus oculi image to approach the third fundus oculi image, aconvergence result (a third last convergence result) obtained by causingthe third fundus oculi image to approach the second fundus oculi image,and a convergence result (a fourth last convergence result) obtained bycausing the second fundus oculi image to approach the first fundus oculiimage. Note that the order of adjustment of the alignment is not limitedto this example. Conversely, adjustment of the alignment may beperformed to approach a plurality of fundus oculi images to the fifthfundus oculi image. The adjustment portion 277 supplies the synthesisportion 226 with a processing target spherical fundus oculi image 293that has been aligned.

Note that, in the above explanation, as a specific example of thealignment, four processing steps, i.e., the vertical/horizontal shift281, the vertical/horizontal stretch 282, the rotation 283 and thezoom-in/zoom-out 284, are performed in this order. However, this ismerely an example and a processing step other than the above-describedprocessing steps may be further performed, or a part of theabove-described processing steps may be omitted. Further when aplurality of processing steps are performed as described above, theprocessing order can be set as desired.

Further, the feature extraction portion 224 and the alignment portion225 may perform alignment using a histogram of an edge portion, asdescribed in, for example, “Shape Matching and Object Recognition UsingShape Contexts”, Serge Belongie, Jitendra Malik, Jan Puzicha, 2002.

Further, any method can be used to determine whether or not thealignment has converged, and a method other than that described abovemay be used. For example, it may be determined that the alignment hasconverged when the difference between the absolute values of the bloodvessel extraction result 291 and the blood vessel extraction result 292is equal to or smaller than a predetermined threshold value.

Note that the alignment using the intersection of blood vessels is alsoperformed basically in the same manner as the alignment using the wholeblood vessels. In other words, the intersection alignment processingportion 234 has basically the same configuration as the blood vesselalignment processing portion 232, and basically performs the sameprocessing, the only difference being whether the biological informationused to perform alignment is the shape of the whole blood vessels or theintersection of the blood vessels.

As described above, since the fundus oculi image is an image of a livingorganism, the fundus oculi image processing device 200 makes use offeatures of the image and thereby performs alignment in the whole imageusing the biological information included in the fundus oculi image. Bydoing this, the fundus oculi image processing device 200 can achievemore accurate alignment more easily.

The configuration of the fundus oculi image processing device 200 isexplained above with reference to FIG. 12 to FIG. 15. Next, fundus oculiimage generation processing that is performed by the fundus oculi imageprocessing device 200 configured in this manner will be explained withreference to FIG. 16.

Flow of Fundus Oculi Image Generation Processing

FIG. 16 is a flowchart illustrating a flow of the fundus oculi imagegeneration processing.

At step S91, the imaging portion 21 reduces the amount of light andcaptures an image of the fundus oculi of the test subject a plurality oftimes. Note that, as described above, image capture of the fundus oculiby the imaging portion 21 may be performed a plurality of times toobtain still images or may be performed once to obtain moving images.

At step S92, the image processing portion 212 causes the input imagebuffer 221 to store data of the plurality of fundus oculi images withlow image quality obtained by the processing at step S91.

At step S93, the fundus oculi sphere conversion portion 222 reads outdata of two sheets of fundus oculi images, which are a processing targetand a comparison target, from the input image buffer 221.

At step S94, the fundus oculi sphere conversion portion 222 applies thewhole processing target fundus oculi image and the whole comparisontarget fundus oculi image that are read out at step S93 to the sphericalmodel stored in the spherical model storage portion 223, and convertsthem into a processing target spherical fundus oculi image and acomparison target spherical fundus oculi image, respectively.

At step S95, the blood vessel extraction portion 231 extracts the shapeand position of the blood vessels from the processing target sphericalfundus oculi image.

The extracted shape and position of the blood vessels are supplied tothe blood vessel alignment processing portion 232 as a blood vesselextraction result.

At step S96, the blood vessel extraction portion 231 extracts the shapeand position of the blood vessels from the comparison target sphericalfundus oculi image. The extracted shape and position of the bloodvessels are supplied to the blood vessel alignment processing portion232 as a blood vessel extraction result.

At step S97, the feature extraction portion 224 determines whether ornot to perform intersection alignment.

When the intersection alignment is not to be performed, NO is determinedat step S97 and the processing proceeds to step 5101. Note thatprocessing from step S101 onward will be described later.

On the other hand, when the intersection alignment is to be performed,YES is determined at step S97 and the processing proceeds to step S98.In this case, the blood vessel extraction portion 231 supplies the bloodvessel extraction results extracted at step S95 and step S96 to theintersection extraction portion 233.

At step S98, the intersection extraction portion 233 extracts anintersection using the blood vessel extraction result of the processingtarget spherical fundus oculi image. The position of the extractedintersection is supplied to the intersection alignment processingportion 234 as an intersection extraction result.

At step S99, the intersection extraction portion 233 extracts anintersection using the blood vessel extraction result of the comparisontarget spherical fundus oculi image. The position of the extractedintersection is supplied to the intersection alignment processingportion 234 as an intersection extraction result.

At step S100, the intersection alignment processing portion 234 performsintersection alignment processing between the processing targetspherical fundus oculi image 251 and the comparison target sphericalfundus oculi image 252, using the intersection extraction resultssupplied at step S98 and step S99.

Note that the intersection alignment processing at step S100 isperformed in the same manner as blood vessel alignment processing thatwill be described later with reference to FIG. 17, except that theintersection of blood vessels is used for alignment instead of the wholeblood vessels. Therefore, an explanation of the intersection alignmentprocessing at step S100 is omitted here to avoid repetition.

At step S101, the blood vessel alignment processing portion 232 performsthe blood vessel alignment processing. More specifically, with respectto the spherical fundus oculi image which has been simply aligned usingthe intersection, the blood vessel alignment processing portion 232further performs blood vessel alignment using the blood vesselextraction results. The blood vessel alignment processing at step S101will be described in more detail later with reference to FIG. 17.

At step S102, the image processing portion 212 determines whether or notall the data of the fundus oculi images have been set as the processingtarget.

When all the data of the fundus oculi images have not yet been set asthe processing target, NO is determined at step 102 and the processingis returned to step S93. Then, processing from step S93 onward isrepeated. More specifically, loop processing from step S93 to step S102is repeated until all the data of the fundus oculi images are set as theprocessing target.

After that, when all the data of the fundus oculi images have been setas the processing target, YES is determined at step 102 and theprocessing proceeds to step S103.

At step S103, the synthesis portion 226 synthesizes data of theplurality of spherical fundus oculi images that have been aligned. As aresult, data of one sheet of a fundus oculi image is generated.

At step S104, the super-resolution processing portion 227 performssuper-resolution processing on the synthesized data of the fundus oculiimage. As a result, data of the fundus oculi image with a higherresolution than that at the time of synthesis at step S103 is generated.

At step S105, the image processing portion 212 causes the storageportion 23 to store the data of the fundus oculi image on which thesuper-resolution processing has been performed, or causes the outputportion 24 to output the data.

This completes the fundus oculi image generation processing. Next, theblood vessel alignment processing at step S101 will be explained.

Flow of Blood Vessel Alignment Processing

FIG. 17 is a flowchart illustrating a flow of the blood vessel alignmentprocessing at step S101 shown in FIG. 16.

At step S111, the superimposition processing portion 271 determineswhether or not the intersection alignment has been performed.

When the intersection alignment has been performed, YES is determined atstep S111 and the processing proceeds to step S112.

At step S112, the superimposition processing portion 271 sets theintersection alignment result as a superimposed result. In accordancewith the superimposed result, the superimposition processing portion 271superimposes the respective blood vessel extraction results of theprocessing target spherical fundus oculi image and the comparison targetspherical fundus oculi image that are supplied from the blood vesselextraction portion 231.

On the other hand, when the intersection alignment has not beenperformed, NO is determined at step S111 and the processing proceeds tostep S113.

At step S113, the superimposition processing portion 271 superimposesthe respective blood vessel extraction results of the processing targetspherical fundus oculi image and the comparison target spherical fundusoculi image.

At step S114, the shift processing portion 272 performs shift alignmentthat shifts the blood vessel extraction result of the processing targetspherical fundus oculi image.

At step S115, the stretch processing portion 273 performs stretchalignment that elongates and contracts the blood vessel extractionresult of the processing target spherical fundus oculi image.

At step S116, the rotation processing portion 274 performs rotationalignment that rotates the blood vessel extraction result of theprocessing target spherical fundus oculi image.

At step S117, the zoom-in/zoom-out processing portion 275 performszoom-in/zoom-out alignment that zooms in or zooms out the blood vesselextraction result of the processing target spherical fundus oculi image.

At step S118, the convergence determination portion 276 determineswhether or not the alignment has converged.

When the alignment has not converged, NO is determined at step S118 andthe processing is returned to step S114. Then, processing from step S114onward is repeated. More specifically, loop processing from step S114 tostep S118 is repeated until the alignment converges.

After that, when the alignment has converged, YES is determined at stepS118 and the processing proceeds to step S119.

At step S119, the adjustment portion 277 adjusts the alignment based oncumulative convergence results obtained until the previous time. As aresult, the aligned processing target spherical fundus oculi image isoutput to the synthesis portion 226.

This completes the blood vessel alignment processing, and the processingproceeds to step S102 shown in FIG. 16.

In this manner, the alignment of the fundus oculi images is performedusing biological information of the photographic subject. Thus, even ina situation in which it is difficult to detect a motion vector, a higherquality fundus oculi image can be obtained while reducing a burden onthe test subject.

Note that the spherical model used in the above-described examples maybe switched to an appropriate spherical model for each test subject inaccordance with conditions of each test subject, such as an eye axislength, eyesight and the like.

Further, the fundus oculi is not a perfect sphere. Therefore, maskprocessing can also be performed on a region that cannot approximate asphere using a spherical model, so that the region is not used formatching processing or alignment processing.

[Application to a Program of the Present Technology]

The series of processes described above can be executed by hardware andcan also be executed by software.

In this case, a personal computer shown in FIG. 18, for example, may beused as at least a part of the above-described image processing device.

In FIG. 18, a CPU 301 performs various types of processing in accordancewith a program stored in a ROM 302. Further, the CPU 301 performsvarious types of processing in accordance with a program that is loadedfrom a storage portion 308 to a RAM 303. Data etc. that is necessary forthe CPU 301 to perform the various types of processing is also stored inthe RAM 303 as appropriate.

The CPU 301, the ROM 302 and the RAM 303 are mutually connected via abus 304. An input output (I/O) interface 305 is also connected to thebus 304.

An input portion 306 that is formed by a keyboard, a mouse and the like,and an output portion 307 that is formed by a display and the like areconnected to the I/O interface 305. Further, the storage portion 308that is formed by a hard disk and the like, and a communication portion309 that is formed by a modem, a terminal adaptor and the like areconnected to the I/O interface 305. The communication portion 309controls communication that is performed with another device (not shownin the drawings) via a network including the Internet.

Further, a drive 310 is connected to the I/O interface 305 according toneed. A removable media 311 that is formed by a magnetic disk, anoptical disk, a magneto optical disk, a semiconductor memory, or thelike is attached as appropriate. Then, a computer program that is readfrom the removable media 311 is installed in the storage portion 308according to need.

When the series of processing is performed by software, a program thatforms the software is installed from a network or a recording medium toa computer that is incorporated in a dedicated hardware, or to, forexample, a general-purpose personal computer that can perform varioustypes of functions by installing various types of programs.

The recording medium that includes this type of program is not onlyformed by the removable media (package media) 311 that is distributedseparately from a main body of the device as shown in FIG. 18 in orderto provide the user with the program, but is also formed by the ROM 302in which the program is recorded and which is provided to the user in astate in which it is incorporated in advance in the main body of thedevice, the hard disk included in the storage portion 308, or the like.The removable media 311 is formed by a magnetic disk (including a floppydisk) in which the program is recorded, an optical disk (including acompact disk-read only memory (CD-ROM) and a digital versatile disk(DVD)), a magneto optical disk (including a mini-disk (MD)), asemiconductor memory, or the like.

Note that, in this specification, steps that write the program to berecorded in the recording medium do not necessarily have to be performedin time series in line with the order of the steps, and instead mayinclude processing that is performed in parallel or individually.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

Additionally, the present technology may also be configured as below.

(1) An image processing device including:

a motion vector detection portion that performs comparison of asubstantially spherical photographic subject such that, among aplurality of captured images including the photographic subject, animage as a processing target and another image as a comparison targetare compared using each of the plurality of captured images as theprocessing target, and which detects a motion vector of a wholethree-dimensional spherical model with respect to the processing target;

a motion compensation portion that performs motion compensation on theprocessing target, based on the motion vector of each of the pluralityof captured images that is detected by the motion vector detectionportion; and

a synthesis portion that synthesizes each of the captured images thatare obtained as a result of the motion compensation performed by themotion compensation portion.

(2) The image processing device according to (1),

wherein with respect to each of a plurality of blocks that are dividedup from the processing target, the motion vector detection portiondetects a local motion vector by performing block matching with thecomparison target, and

wherein the motion vector detection portion detects the motion vector ofthe whole three-dimensional spherical model with respect to theprocessing target, using the local motion vector of each of theplurality of blocks.

(3) The image processing device according to (1) or (2),

wherein the motion vector detection portion converts the local motionvector with respect to each of the plurality of blocks in the processingtarget into a local spherical motion vector in the three-dimensionalspherical model, and

wherein the motion vector detection portion detects the motion vector ofthe whole three-dimensional spherical model with respect to theprocessing target, using the local spherical motion vector of each ofthe plurality of blocks.

(4) The image processing device according to (1), (2), or (3)

wherein the motion vector detection portion converts each of theplurality of blocks in the processing target into a plurality ofspherical blocks in the three-dimensional spherical model,

wherein with respect to each of the plurality of spherical blocks, themotion vector detection portion detects, as the local motion vector, alocal spherical motion vector by performing block matching with thecomparison target, and

wherein the motion vector detection portion detects the motion vector ofthe whole three-dimensional spherical model with respect to theprocessing target, using the local spherical motion vector of each ofthe plurality of spherical blocks.

(5) The image processing device according to any one of (1) to (4),

wherein the motion vector detection portion converts each of theprocessing target and the comparison target into a spherical image inthe three-dimensional spherical model, and

wherein the motion vector detection portion performs matching betweenthe spherical image of the processing target and the spherical image ofthe comparison target, and thereby detects the motion vector of thewhole three-dimensional spherical model with respect to the processingtarget.

(6) The image processing device according to any one of (1) to (5),

wherein the photographic subject is a fundus oculi.

(7) The image processing device according to any one of (1) to (6),

wherein the three-dimensional spherical model is switched and used inaccordance with conditions of the photographic subject.

(8) An image processing device including:

a conversion portion that, among a plurality of captured images thatinclude a substantially spherical photographic subject, converts animage as a processing target and another image as a comparison targetinto spherical images on a three-dimensional spherical model, using eachof the plurality of captured images as the processing target;

an extraction portion that extracts features of each of the sphericalimage of the processing target and the spherical image of the comparisontarget;

an alignment portion that aligns positions of the features such that thefeatures match each other; and

a synthesis portion that synthesizes each of the captured images thatare obtained as a result of the alignment performed by the alignmentportion.

(9) The image processing device according to (8),

wherein a blood vessel shape is used as the feature.

(10) The image processing device according to (8) or (9),

wherein the photographic subject is a fundus oculi.

(11) The image processing device according to (8), (9), or (10),

wherein the three-dimensional spherical model is switched and used inaccordance with conditions of the photographic subject.

The present technology can be applied to an image processing device.

The present disclosure contains subject matter related to that disclosedin Japanese Priority Patent Application JP 2011-188277 filed in theJapan Patent Office on Aug. 31, 2011, the entire content of which ishereby incorporated by reference.

What is claimed is:
 1. An image processing device comprising: a motionvector detection portion that performs comparison of a substantiallyspherical photographic subject such that, among a plurality of capturedimages including the photographic subject, an image as a processingtarget and another image as a comparison target are compared using eachof the plurality of captured images as the processing target, and whichdetects a motion vector of a whole three-dimensional spherical modelwith respect to the processing target; a motion compensation portionthat performs motion compensation on the processing target, based on themotion vector of each of the plurality of captured images that isdetected by the motion vector detection portion; and a synthesis portionthat synthesizes each of the captured images that are obtained as aresult of the motion compensation performed by the motion compensationportion.
 2. The image processing device according to claim 1, whereinwith respect to each of a plurality of blocks that are divided up fromthe processing target, the motion vector detection portion detects alocal motion vector by performing block matching with the comparisontarget, and wherein the motion vector detection portion detects themotion vector of the whole three-dimensional spherical model withrespect to the processing target, using the local motion vector of eachof the plurality of blocks.
 3. The image processing device according toclaim 2, wherein the motion vector detection portion converts the localmotion vector with respect to each of the plurality of blocks in theprocessing target into a local spherical motion vector in thethree-dimensional spherical model, and wherein the motion vectordetection portion detects the motion vector of the wholethree-dimensional spherical model with respect to the processing target,using the local spherical motion vector of each of the plurality ofblocks.
 4. The image processing device according to claim 2, wherein themotion vector detection portion converts each of the plurality of blocksin the processing target into a plurality of spherical blocks in thethree-dimensional spherical model, wherein with respect to each of theplurality of spherical blocks, the motion vector detection portiondetects, as the local motion vector, a local spherical motion vector byperforming block matching with the comparison target, and wherein themotion vector detection portion detects the motion vector of the wholethree-dimensional spherical model with respect to the processing target,using the local spherical motion vector of each of the plurality ofspherical blocks.
 5. The image processing device according to claim 1,wherein the motion vector detection portion converts each of theprocessing target and the comparison target into a spherical image inthe three-dimensional spherical model, and wherein the motion vectordetection portion performs matching between the spherical image of theprocessing target and the spherical image of the comparison target, andthereby detects the motion vector of the whole three-dimensionalspherical model with respect to the processing target.
 6. The imageprocessing device according to claim 1, wherein the photographic subjectis a fundus oculi.
 7. The image processing device according to claim 6,wherein the three-dimensional spherical model is switched and used inaccordance with conditions of the photographic subject.
 8. An imageprocessing method comprising: performing comparison of a substantiallyspherical photographic subject such that, among a plurality of capturedimages including the photographic subject, an image as a processingtarget and another image as a comparison target are compared using eachof the plurality of captured images as the processing target, anddetecting a motion vector of a whole three-dimensional spherical modelwith respect to the processing target; performing motion compensation onthe processing target, based on the motion vector of each of theplurality of captured images that is detected by processing of themotion vector detecting step; and synthesizing each of the capturedimages that are obtained as a result of the motion compensationperformed by processing of the motion compensating step.
 9. A recordingmedium that stores a program comprising: performing comparison of asubstantially spherical photographic subject such that, among aplurality of captured images including the photographic subject, animage as a processing target and another image as a comparison targetare compared using each of the plurality of captured images as theprocessing target, and detecting a motion vector of a wholethree-dimensional spherical model with respect to the processing target;performing motion compensation on the processing target based on themotion vector of each of the plurality of captured images; andsynthesizing each of the captured images that are obtained as a resultof the motion compensation.
 10. A program that causes a computer toperform control processing comprising: performing comparison of asubstantially spherical photographic subject such that, among aplurality of captured images including the photographic subject, animage as a processing target and another image as a comparison targetare compared using each of the plurality of captured images as theprocessing target, and detecting a motion vector of a wholethree-dimensional spherical model with respect to the processing target;performing motion compensation on the processing target based on themotion vector of each of the plurality of captured images; andsynthesizing each of the captured images that are obtained as a resultof the motion compensation.
 11. An image processing device comprising: aconversion portion that, among a plurality of captured images thatinclude a substantially spherical photographic subject, converts animage as a processing target and another image as a comparison targetinto spherical images on a three-dimensional spherical model, using eachof the plurality of captured images as the processing target; anextraction portion that extracts features of each of the spherical imageof the processing target and the spherical image of the comparisontarget; an alignment portion that aligns positions of the features suchthat the features match each other; and a synthesis portion thatsynthesizes each of the captured images that are obtained as a result ofthe alignment performed by the alignment portion.
 12. The imageprocessing device according to claim 11, wherein a blood vessel shape isused as the feature.
 13. The image processing device according to claim12, wherein the photographic subject is a fundus oculi.
 14. The imageprocessing device according to claim 11, wherein the three-dimensionalspherical model is switched and used in accordance with conditions ofthe photographic subject.
 15. An image processing method comprising:converting, among a plurality of captured images that include asubstantially spherical photographic subject, an image as a processingtarget and another image as a comparison target into spherical images ona three-dimensional spherical model, using each of the plurality ofcaptured images as the processing target; extracting features of each ofthe spherical image of the processing target and the spherical image ofthe comparison target; aligning positions of the features such that thefeatures match each other; and synthesizing each of the captured imagesthat are obtained as a result of the alignment performed by processingof the aligning step.
 16. A recording medium that stores a programcomprising: converting, among a plurality of captured images thatinclude a substantially spherical photographic subject, an image as aprocessing target and another image as a comparison target intospherical images on a three-dimensional spherical model, using each ofthe plurality of captured images as the processing target; extractingfeatures of each of the spherical image of the processing target and thespherical image of the comparison target; aligning positions of thefeatures such that the features match each other; and synthesizing eachof the captured images that are obtained as a result of the alignment.17. A program that causes a computer to perform control processingcomprising: converting, among a plurality of captured images thatinclude a substantially spherical photographic subject, an image as aprocessing target and another image as a comparison target intospherical images on a three-dimensional spherical model, using each ofthe plurality of captured images as the processing target; extractingfeatures of each of the spherical image of the processing target and thespherical image of the comparison target; aligning positions of thefeatures such that the features match each other; and synthesizing eachof the captured images that are obtained as a result of the alignment.